كليدواژه :
ديناميك شهري , ماشين هاي خودكار سلولي , زنجيره ماركوف , سيستم هاي اطلاعات جغرافيايي , شهر مراغه
چكيده فارسي :
در دهه هاي اخير همگام با رشد شهرنشيني، مدل هاي مختلفي جهت بررسي و پيش بيني رشد شهري بكار گرفته شده است.در اين زمينه، ماشين هاي خودكار سلولي در چهارچوب رويكرد سيستمي وارد مباحث جغرافيايي شد. مدل هاي مختلفي به منظور ايجاد قوانين گذار در ماشين هاي خودكار سلولي تركيب مي شوند كه در اين زمينه مي توان به زنجيره ماركوف اشاره كرد.در زنجيره ماركوف،حالت آينده يك سيستم تنها وابسته به حالت پيشين سيستم است، درحاليكه در ماشين هاي خودكار سلولي،حالت آينده يك سيستم نه تنها وابسته به حالت سابق، بلكه وابسته به حالت همسايگان نيز مي باشد و همين ويژگي است كه به آن ماهيت فضايي يا جغرافيايي مي بخشد. پايش الگوي رشد شهر درطي تقريبا 30 سال گذشته روند توسعه شهر مراغه عمدتا در جهات شرقي و بر روي زمين هاي باير رانشان مي دهد.شهر مراغه در بين سال هاي 1369 تا 1379 يك رشد نسبتا سريعي را تجربه كرده و پس از آن نيز به صورت پيوسته عمدتا به سمت شرق توسعه يافته است. شبيه سازي فضايي الگوي رشد شهر نشان مي دهد كه در سال هاي آتي اين روند همچنان تداوم خواهد يافت. بطوريكه در طي 17 سال آينده 774 هكتار از اراضي باير و ديم و درحدود 417 هكتار از اراضي كشاورزي و باغات به ساخت وسازهاي شهري تبديل خواهند شد. همپوشاني فازي لايه هاي مختلف نشان مي دهد كه مكان يابي اوليه توسعه فيزيكي شهر مراغه به طور مناسبي صورت گرفته و بهتر است روند فعلي توسعه با تمايل بيشتر به سمت جنوب شرق، اجتناب از ساخت و ساز در شيب هاي تند و همچنين جلوگيري از تغيير كاربري اراضي كشاورزي به شهري تداوم يابد.
چكيده لاتين :
Introduction
In coming decades, the rapid increase of large cities in the developing world and the transformation of urban landscapes in the developed world will be among the greatest challenges to human welfare and a viable global environment. In this context, there are several approaches for modeling urban development. In the past three decades, studies of nonlinear process and open systems have led to the emergence of new understandings of complex systems and their evolution. Based on these understandings, cities are looked at as complex and open systems that have the capability of self-organization. Urban models based on the automata technique have also emerged under the paradigm of a self-organizing system, with cellular automata (CA) being the simplest but most popular in action. In this research, the spatial expansion of Maragheh city was simulated using Cellular automata- Markov chain hybrid model.
Metodology
White (1998) defined a CA as ‘‘a discrete cell space, together with a set of possible cell states and a set of transition rules that determine the state of each cell as a function of the states of all cells within a defined cell-space neighborhood of the cell’’. In the CA framework, dynamics are represented as a change in the state of grid cells from one time step to the following time step. The cell need not, however, necessarily change its state. What happens to each grid cell is defined by a transition rule or transition rules. If the transition rule requires that the state of a grid cell is only dependent on its state at a previous time step, such a model is called a Markov model, and is not considered a CA model. Cellular automata models have one additional feature: the transition rules operate on cells based on the local neighborhood of those cells. For example, in a 2D grid, the state of a cell at time t+1 could be a function of the states of the cells to the north, south, east, and west of the cell of interest at time t.
Result and Discussion
- Transition probability matrix
Transition probabilities express the likelihood that a pixel of a given class will change to any other class (or stay the same) in the next time period. For study area, the transition probability matrix of land covers have been presented in table1. In comparison with other classes, transition probability of class4 (barren lands and dry farming) state to other class states is higher. - Transition areas matrix
This expresses the total area (in cells) expected to change in the next time period. According to the transition areas matrix values (table2) is expected to the largest areas of transition in the study area related to transition of class2 and class4 to class1. So that, 8600 cells of class4 (about 774 hectares) and 4635 cells of class 2 (approximately 417 hectares) will change to class1 state.- Urban Cellular automata and creating of suitability maps
In this study, another transition rules were designed using multi criteria evaluation and fuzzy membership functions. All layers that were obtained by applying these rules overlapped and suitability map was obtained for each class and then entered to the model. In relation to the neighborhood, Von Neumann neighborhood was used as a 5×5 proximity Filter. According to output of cellular automata- Markov chain model, spatial expansion of city in the coming years will be almost similar to previous trend, and city mainly be expanded towards barren lands in the east of study area.
Conclusion
Results represent high efficiency of Cellular automata- Markov chain in the urban spatial growth simulation. In the past three decades, development trend of Maragheh city has been more towards barren lands. According to the output of the model, this trend will continue over the next 17 years. So that, the city will be expended due to the transition of barren lands cells state to urban cells state, and approximately 774 hectares from surrounding barren lands will be converted to urban lands. However, with continue of the previous trend, nearly 417 hectares of good agricultural lands will also change to urban lands.
Suggested that in order to Maragheh city physical development prevent the agricultural lands destruction. Especially the agricultural lands surrounding the city are mostly orchards, and have great importance in supplying the needs of city to the agricultural products, tourism, and reduce air pollution. In this context, it is desirable that city developed primarily in the form of town establishing in the South East of study area. However, it is necessary to consider the risk of flooding in these areas.
